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Summary Civil engineering structures, such as reinforced concrete frames, exhibit nonlinear behavior when subject to dynamic loads, such as earthquakes. The ability to detect damages in structures after a major earthquake will ensure their reliability and safety. Innovative analysis techniques for damage detection of structures have been extensively studied recently. However, practical and effective local damage identification techniques remain to be developed for nonlinear structures, in particular hysteretic reinforced concrete (RC) structures. In this paper, a smooth hysteretic model with stiffness and strength degradations and with the pinching effect is used to represent the dynamic characteristics of RC frames. A system identification method capable of detecting damages in nonlinear structures, referred to as the adaptive quadratic sum-square error with unknown inputs (AQSSE-UI), will be used to detect damages in hysteretic RC frames. The performance of the AQSSE-UI technique will be demonstrated by the experimental data. A one-third-scale two-story RC frame has been tested experimentally on the shake table at National Center for Research on Earthquake Engineering, Taiwan. This two-story RC frame was subject to different levels of ground excitations back to back. The RC frame is firstly considered as a time-varying linear model with rotational springs at joints, and the tracking of the degradation of the time-varying stiffness parameters is carried out using the AQSSE-UI technique. Then the same RC frame is considered as a nonlinear structure consisting of plastic hinges at joints following a smooth hysteretic model. Experimental results show that the AQSSE-UI technique is quite effective for tracking (i) the stiffness degradation of time-varying linear structures and (ii) the nonlinear hysteretic parameters with stiffness and strength degradations as well as the pinching effect. Copyright © 2015 John Wiley & Sons, Ltd.

SUMMARY The objective of this paper is to develop methods for extracting trends from long-term static deformation data of a dam and try to set an early warning threshold level on the basis of the results of analyses. The static deformation of a dam is mainly influenced by the water pressure (or water level) of the dam and the temperature distribution of the dam body. The relationship among the static deformation, the water level, and the temperature distribution of the dam body is complex and unknown; therefore, it can be approximated by static neural networks. Although the static deformation almost has no change during a very short time, it changes with time for long-term continuous observation. Therefore, long-term static deformation can be approximated dynamically using dynamic neural networks. Moreover, static deformation data is rich, but information is poor. Linear and nonlinear principal component analyses are particularly well suited to deal with this kind of problem. With these reasons, different approaches are applied to extract features of the long-term daily based static deformations of the Fei-Tsui arch dam (Taiwan). The methods include the static neural network, the dynamic neural network, principal component analysis, and nonlinear principal component analysis. Discussion of these methods is made. By using these methods, the residual deformation between the estimated and the recorded data are generated, and through statistical analysis, the threshold level of the static deformation of a dam can be determined on the basis of the normality assumption of the residual deformation. Copyright © 2011 John Wiley & Sons, Ltd.

SUMMARY The behavior of story-isolated structures during an earthquake is highly affected by the characteristics of the isolation system and its location along the height of the building. Interaction between the super-structure and the sub-structure with mid-story isolation as nonlinear connector makes the mid-story isolation building even more complicated during the earthquake excitation. In this paper, two different identification techniques are applied to identify the time-varying dynamic characteristics of the mid-story isolation building using the ambient vibration test data as well as the earthquake response data. For the ambient vibration measurements, the recursive stochastic subspace identification method is used, whereas for the earthquake response data the recursive subspace identification is used. Comparison on dynamic characteristics of the story-isolated building from the results of identification is made, and the effect of nonlinear behavior of the mid-isolation system to the response of the building during the earthquake is discussed. Copyright © 2011 John Wiley & Sons, Ltd.

From the point of view of structural safety monitoring, the state of damage after severe loading becomes an important issue for safety evaluation. In this study, seismic response data of a total of four reinforced concrete frames, designed with same dimensions and same seismic design code, subject to different levels of ground excitation individually from shaking table test, are used to study the nonlinear and damage pattern of reinforced concrete structure. First, the signal-based damage identification method, which include the change of the Hilbert marginal spectrum, the instantaneous frequency and phase difference, the Holder exponent, and the wavelet component energy distribution, is applied to identify the nonlinear features of the structure. Then the general Bouc–Wen hysteretic model was used to identify the physical parameters such as stiffness degradation, strength deterioration and hysteresis behavior of the reinforced concrete frame. Finally, the relationship between the change of model properties, the global damage index and the identified damage features are investigated. The relationship between the identified stiffness degradation function and the identified structural nonlinear/damage features can be used as a safety assessment index for the seismic assessment of reinforced concrete structure. Copyright © 2010 John Wiley & Sons, Ltd.

An identification method for estimating the time varying excitation force acting on a structural system based on its response measurements is presented in this study. The method employs the simple Kalman filter to establish a regression model between the residual innovation and the input excitation forces. In applying the method, first, the ambient vibration measurement of a structural system is collected, then the stochastic subspace identification is applied to estimate the system matrix ‘A’ and the measurement matrix ‘H’. Incorporated with the identified ‘A’ and ‘H’ matrices the dynamic excitation forces are estimated from the measured structural responses by using Kalman filter with a recursive least-square estimator to update the estimation in the sense of real-time computation. Verification of the method on numerical simulation through MIMO system is conducted first. Identification of soil forces during the shaking table test of soil–pile interaction is also demonstrated. Copyright © 2008 John Wiley & Sons, Ltd.

Wireless sensor networks have rapidly matured in recent years to offer data acquisition capabilities on par with those of traditional tethered data acquisition systems. Entire structural monitoring systems assembled from wireless sensors have proven to be low cost, easy to install, and accurate. However, the functionality of wireless sensors can be further extended to include actuation capabilities. Wireless sensors capable of actuating a structure could serve as building blocks of future generations of structural control systems. In this study, a wireless sensor prototype capable of data acquisition, computational analysis and actuation is proposed for use in a real-time structural control system. The performance of a wireless control system is illustrated using a full-scale structure controlled by a semi-active magnetorheological (MR) damper and a network of wireless sensors. One wireless sensor designated as a controller automates the task of collecting state data, calculating control forces, and issuing commands to the MR damper, all in real time. Additional wireless sensors are installed to measure the acceleration and velocity response of each system degree of freedom. Base motion is applied to the structure to simulate seismic excitations while the wireless control system mitigates inter-storey drift response of the structure. An optimal linear quadratic regulation solution is formulated for embedment within the computational cores of the wireless sensors. Copyright © 2007 John Wiley & Sons, Ltd.

This paper presents the cable vibration problem of structural control for a cable-stayed bridge. The goal of this study is not only to provide a test for development of strategies for control of cable vibration, but also to examine the effect of cable vibration on control of the structure itself. Based on the detailed drawings of the Gi-Lu bridge, a three-dimensional numerical model has been developed to represent complex behavior of the full-scale bridge. Differential motion at multiple supports and cable flexibility are included in the analysis. A MATLAB-based structural analysis tool has been developed that considers of geometrically nonlinear behavior of beam elements that represent the cables. The dynamic characteristics of cables are verified using field experiments, a commercial finite element code, and the MATLAB program. Evaluation criteria are presented for the design problems that are consistent with the goal of control of seismic response control of a cable-stayed bridge. Control devices are assumed to be installed either between the deck and the end-abutment and/or between the deck and cables. Passive and active devices are used to study behavior of the model. A comparison of the control responses using truss elements or using geometrically nonlinear beam elements to represent the cable is also presented. Copyright © 2005 John Wiley & Sons, Ltd.

A series of large-scale experimental tests is conducted on a mass equipped with a base-isolation system that consists of high damping rubber bearings (HDRB) and a 300 kN magnetorheological (MR) damper. The 21 772 kg mass and its hybrid isolation system are subjected to various intensities of near- and far-fault earthquakes on a large shake table. Three proposed fuzzy controllers use feedback from displacement, velocity, or acceleration transducers attached to the structure to modulate resistance of the semi-active damper to motion. Results from various types of passive and semi-active control strategies are summarized and compared. The study shows that a combination of HDRB isolators and an adjustable MR damper can provide robust control of vibration for large civil engineering structures that need protection from a wide range of seismic events. Low power consumption, direct feedback, high reliability, energy dissipation, and fail-safe operation are validated in this study. Copyright © 2006 John Wiley & Sons, Ltd.

This paper presents the performance assessment of the semi-active control on the ASCE benchmark model of a cable-stayed bridge using MR dampers. In order to develop the semi-active control algorithm unique features of the MR damper were generated which considered the intrinsic nonlinear behavior of the damper. The modified nonlinear hysteretic bi-viscous model with three slopes in the force/velocity loop and two yield force values were proposed. Different control algorithms were selected including H2, H∞ and mix H2 and H∞ control algorithms, sliding mode control in lieu of the LQG formulation, and the SMC with fuzzy logic control. The performance of the control algorithm is compared through a numerical study of the ASCE benchmark model of a cable-stayed bridge. Finally, the control performance among different control algorithm is examined. Through numerical study it is concluded that the mixed H2 and H∞ semi-active control method is the better control method because it has a good ability to mitigate every evaluated response, and has a robust control gain to generate the appropriate control force. But the passive control system (passive-on for MR-damper) for this benchmark problem also provides good control effectiveness. Copyright © 2005 John Wiley & Sons, Ltd.

Summary The goal of this study was to investigate the use of vibration-based system identification methods for detecting wind turbine blade delamination during operating conditions and when subjected to other excitations such as earthquake ground motion. In particular, multivariate singular spectrum analysis and two subspace identification techniques were tested for detecting the dynamic characteristics of the wind blade. A total of two series of experiments was conducted to verify the proposed algorithms. In both cases, accelerometers were installed on the blades for measuring their vibration response. The first test series were performed in the laboratory; a motor spun a small-scale customized wind turbine at controlled angular velocities, and the shaking table excited the entire structure. Artificial damage was introduced by loosening bolts at the blade-rotor connection. The second test was conducted in the field, and vibration data was collected from an operating small-scale wind turbine. The blade's vibration response was analyzed through time-frequency analyses and subspace identification. These tests confirmed that the estimation of dynamic characteristics of blade and rotating frequency of the turbine system was feasible, and the results will guide future monitoring studies planned for larger-scale systems. Copyright © 2016 John Wiley & Sons, Ltd.

Damage detection in structural health monitoring should accommodate the variation caused by varying environmental conditions such as temperature, humidity, loading, and boundary conditions. A structural damage detection technique is proposed to deal with the continuous monitoring data of a structural system subjected to the complex nonlinear behavior caused by varying environmental conditions. Based on the identified or measured target features of the structural system under varying environmental conditions, e.g. stiffness of the structural components, the nonlinear principal component analysis (NLPCA) using auto-associative neural network is performed to extract the underlying environmental factors. Then a prediction model for NLPCA is proposed to estimate the damage extent. This proposed technique is capable of dealing with not only the non-increasing features, e.g. stiffness, but also the non-decreasing features, e.g. damage index, after damage is introduced without measuring the environmental factors directly. A numerical study is performed to demonstrate the advantages of the proposed technique over the technique using principal component analysis. A synthetic bridge model is simulated with the consideration of a specific element stiffness reduction together with the change due to environmental conditions including temperature, gradient of temperature, humidity, and frozen supports. Results show that the extent of stiffness loss can be quantified accurately and promptly after the damage is introduced. Copyright © 2009 John Wiley & Sons, Ltd.